Date of Award
Accurate rainfall forecasting using weather radar imagery has always been a crucial and predominant task in the field of meteorology , ,  and . Competitive Radial Basis Function Neural Networks (CRBFNN)  is one of the methods used for weather radar image based forecasting. Recently, an alternative CRBFNN based approach  was introduced to model the precipitation events. The difference between the techniques presented in  and  is in the approach used to model the rainfall image. Overall, it was shown that the modified CRBFNN approach  is more computationally efficient compared to the CRBFNN approach . However, both techniques  and  share the same prediction stage. In this thesis, a different GRBFNN approach is presented for forecasting Gaussian envelope parameters. The proposed method investigates the concept of parameter dependency among Gaussian envelopes. Experimental results are also presented to illustrate the advantage of parameters prediction over the independent series prediction.
Kattekola, Sravanthi, "Weather Radar image Based Forecasting using Joint Series Prediction" (2010). University of New Orleans Theses and Dissertations. 1238.